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Recent Advances of Chimp Optimization Algorithm:Variants and Applications

Journal of Bionic engineering 2023年第6期20卷 2840-2862页

作者： Mohammad Sh.Daoud Mohammad Shehab Laith Abualigah Mohammad Alshinwan Mohamed Abd Elaziz Mohd Khaled Yousef Shambour Diego Oliva Mohammad AAlia Raed Abu Zitar College of Engineering Al Ain University112612Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman11953Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia Faculty of Information Technology Applied Science Private UniversityAmman 11931Jordan Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 7120001Egypt The Custodian of the Two Holy Mosques Institute for Haj and Umrah Research Umm Al-Qura UniversityMakkah 24382Saudi Arabia Depto.de Innovacio'n Basada en la Informacio'n y el Conocimiento Universidad de GuadalajaraCUCEI45129 GuadalajaraMexico Cyber Security Department Faculty of Science and Information TechnologyAl Zaytoonah University of JordanAmman 11733Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044 Abu DhabiUnited Arab Emirates School of Engineering and Technology Sunway University Malaysia27500Petaling JayaMalaysia

Chimp Optimization Algorithm(ChOA)is one of the recent metaheuristics swarm intelligence *** has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods:it has very few parameters,and no derivation information is required in the initial ***,it is simple,easy to use,flexible,scalable,and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable ***,the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short ***,in this review paper,several research publications using ChOA have been overviewed and ***,introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual *** main operations of ChOA are procedurally discussed,and the theoretical foundation is ***,the recent versions of ChOA are discussed in detail which are categorized into modified,hybridized,and paralleled *** main applications of ChOA are also thoroughly *** applications belong to the domains of economics,image processing,engineering,neural network,power and energy,networks,*** of ChOA is also *** review paper will be helpful for the researchers and practitioners of ChOA belonging to a wide range of audiences from the domains of optimization,engineering,medical,data mining,and *** well,it is wealthy in research on health,environment,and public ***,it will aid those who are interested by providing them with potential future research.

关键词： Artificial intelligence Nature-inspired optimization algorithms Chimp optimization algorithm Optimization problems

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Exploring Tourist Feedback on Riau Attractions Through Indonesian Language YouTube Opinion Using Naïve Bayes Algorithm

Exploring Tourist Feedback on Riau Attractions Through Indon...

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2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications technology, IAICT 2023

作者： Kurniawan, Rahmad Iskandar, Iwan Lestari, Fitra Harpizon, Habibi Al Rasyid Husti, Ilyas Faculty of Mathematics and Natural Sciences Universitas Riau Department of Computer Science Pekanbaru Indonesia Universitas Islam Negeri Sultan Syarif Kasim Riau Department of Informatics Engineering Pekanbaru Indonesia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi Malaysia Graduate Program Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (纸本)9798350313635

YouTube is a widely-used platform in Indonesia, with 93.8% of its users. As such, it presents a valuable opportunity for marketing tourist destinations, particularly in Riau province, which aims to become Indonesia's top Halal travel destination. Tourism is a vital contributor to the economic growth of regions, and each province in Indonesia competes to promote its tourist attractions to attract more visitors every year. However, the large volume of data can challenge the manual analysis of feedback from YouTube's features, such as likes, dislikes, and comments. A literature review suggests that the Naive Bayes algorithm, which uses machine learning, is helpful for sentiment analysis. Therefore, this study aims to analyze public sentiment toward tourist destinations in Riau province by analyzing YouTube comments using the Naïve Bayes algorithm. The study used 1680 opinions collected from 10 YouTube videos showcasing tourist destinations in Riau. The Naive Bayes algorithm classified 60% of the comments as positive, 32% as neutral, and 8% as negative. The experimental results indicated an accuracy and precision of 73%, a recall of 94%, and an F-1 Score of 82%. The study used the word frequency technique to reveal that Riau could become a popular halal tourist destination based on several frequently occurring words in the comments. © 2023 IEEE.

关键词： Sentiment analysis

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Designing a Smart Garden for Automated Plant Watering using Flutter and Internet of Things in the Context of Industry 4.0 17

Designing a Smart Garden for Automated Plant Watering using ...

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17th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2023

作者： Manaf, Khaerul Rahman, Aedah Binti Abd Setiawan, Yadi Kaffah, Faiz M Lukman, Nur Pitoyo, Djoko Universitas Sangga Buana Faculty of Engineering Department of Information System Bandung Indonesia Asia e University Department of Information Communication Technology Kaulalumpur Malaysia Faculty of Science and Technology State Islamic University Sunan Gunung Djati Department of Informatics Bandung Indonesia Intitut Agama Islam Negeri Syekh Nurjati Cirebon Indonesia Universitas Sangga Buana Bandung Indonesia

ISBN: (纸本)9798350309164

Plant maintenance in educational institutions often needs consistent watering practices, leading to plant withering and decline. This research endeavors to develop an intelligent gardening system employing Flutter and the Internet of Things (IoT) to automate plant watering based on soil moisture levels. The research focuses on loose soil conditions, categorized through laboratory testing at a high school, with classifications ranging from 0.3V-1.27V for water-rich soil, 1.28V-1.79V for wet soil, 1.80V-2.60V for dry ground, and 2.61V-2.90V for parched earth. This study implements IoT principles into an Android application to enable automated plant watering when soil moisture levels indicate dryness © 2023 IEEE.

关键词： Internet of things

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Deep Learning Based License Plate Number Recognition for Smart Cities

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Computers, Materials & Continua 2022年第1期70卷 2049-2064页

作者： T.Vetriselvi E.Laxmi Lydia Sachi Nandan Mohanty Eatedal Alabdulkreem Shaha Al-Otaibi Amal Al-Rasheed Romany F.Mansour Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTrichy620002India Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science&Engineering Vardhaman College of Engineering(Autonomous)Hyderabad501218India Department of Informatics and Computing Singidunum UniversitySerbia Computer Science Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Information Systems Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended *** controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city *** the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic *** VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number *** current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license *** proposed model involves two main stages namely,license plate detection and Tesseract-based character *** detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 ***,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)*** performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.

关键词： Deep learning smart city tesseract computer vision vehicle license plate recognition

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Logo Detection Plagiarism Using Generative Adversarial Network

Logo Detection Plagiarism Using Generative Adversarial Netwo...

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International Conference on informatics and Computing (ICIC)

作者： Rizky Rivanni Elviawaty Muisa Zamzami Rahmat Widia Sembiring Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Engineering and Informatics Politeknik Negeri Medan Medan Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Logo, as a graphic form and visual representation of a company, organization, or brand, has been widely utilized and disseminated across the internet. This accessibility makes logo easily replicable, allowing for the imitation and reuse of concepts or forms for creating new logo. Therefore, it is required to have a system that is able to analyze and identify the similarity between logos. In this study, logo plagiarism detection is proposed by using Generative Adversarial Network based on CycleGAN approach. The logo training process involves generator and discriminator networks as part of the GAN process to recognize the logos characteristics. The trained GAN model is tested by generating logo images using the model weights and inputting comparison logos to test their similarity through Template Matching method for plagiarism detection. From the generated test logo results, the logo produced by the generator closely resemble the original logo. The training results using GAN based on CycleGAN approach yielded average loss value of Generator A 6.3572, Generator B 6.1541, Discriminator A 0.6378 and Discriminator B 0.7186. The lowest logo plagiarism analysis value was obtained with an accuracy of 20.11% for logos with gradual changes in shape and different perspectives. The highest accuracy of 93.40% was achieved for logos with no changes at all.

关键词： Training Visualization Accuracy Shape Plagiarism Companies Generative adversarial networks Generators Character recognition informatics

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A Heterogeneous Network-based Contrastive Learning Approach for Predicting Drug-Target Interaction

A Heterogeneous Network-based Contrastive Learning Approach ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Hu, Junwei Bewong, Michael Kwashie, Selasi Zhang, Wen Nofong, Vincent M. Wu, Guangsheng Feng, Zaiwen Huazhong Agricultural University College of Informatics Hubei Wuhan China Charles Sturt University School of Computing Mathematics & Engineering Wagga WaggaNSW Australia Charles Sturt University Artificial Intelligence & Cyber Futures Institute BathurstNSW Australia University of Mines and Technology Faculty of Engineering Tarkwa Ghana Xinyu University School of Mathematics and Computer Science China

ISBN: (纸本)9798350386226

Drug-target interaction (DTI) prediction is crucial for drug development and repositioning. Methods using heterogeneous graph neural networks (HGNNs) for DTI prediction have become a promising approach, with attention-based models often achieving excellent performance. However, these methods typically overlook edge features when dealing with heterogeneous biomedical networks. We propose a heterogeneous network-based contrastive learning method called HNCL-DTI, which designs a heterogeneous graph attention network to predict potential/novel DTIs. Specifically, our HNCL-DTI utilizes contrastive learning to collaboratively learn node representations from the perspective of both node-based and edge-based attention within the heterogeneous structure of biomedical networks. Experimental results show that HNCL-DTI outperforms existing advanced baseline methods on benchmark datasets, demonstrating strong predictive ability and practical effectiveness. The data and source code are available at https://***/Zaiwen/HNCL-DTI. © 2024 IEEE.

关键词： Contrastive Learning

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TECHNIQUES FOR ENHANCING MEMORY CAPACITY OF RESERVOIR COMPUTING

arXiv

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arXiv 2025年

作者： Yokota, Atsuki Kawashima, Ichiro Saito, Yohei Tamukoh, Hakaru Nomura, Osamu Morie, Takashi Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Japan Research Center for Neuromorphic AI Hardware Kyushu Institute of Technology Japan Faculty of Informatics The University of Fukuchiyama Japan

Reservoir Computing (RC) is a bio-inspired machine learning framework, and various models have been proposed. RC is a well-suited model for time series data processing, but there is a trade-off between memory capacity and nonlinearity. In this study, we propose methods to improve the memory capacity of reservoir models by modifying their network configuration except for the inside of reservoirs. The Delay method retains past inputs by adding delay node chains to the input layer with the specified number of delay steps. To suppress the effect of input value increase due to the Delay method, we divide the input weights by the number of added delay steps. The Pass through method feeds input values directly to the output layer. The Clustering method divides the input and reservoir nodes into multiple parts and integrates them at the output layer. We applied these methods to an echo state network (ESN), a typical RC model, and the chaotic Boltzmann machine (CBM)-RC, which can be efficiently implemented in integrated circuits. We evaluated their performance on the NARMA task, and measured information processing capacity (IPC) to evaluate the trade-off between memory capacity and nonlinearity. Copyright © 2025, The Authors. All rights reserved.

关键词： Memory management

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Revealing the Imperceptible Network: Numerical Studies on the Impact of Electromagnetism on the Human Brain

Revealing the Imperceptible Network: Numerical Studies on th...

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International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Lazhar Bouali Kassem Aniss Bouali Department of Industrial Engineering Faculty of Science and Technology University of “Abbas Laghrour” Khenchela Khenchela Province Algeria Faculty of Informatics University of Debrecen. Kassai út 26 Debrecen Hungary

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Recent advancements in radiofrequency electromagnetic field communication have raised questions about their potential impact on biological tissues, particularly the human brain. Our study aims to numerically examine the influence of these electromagnetic fields on the human brain. For this purpose, we used a numerical modeling approach that integrates the human brain as the internal medium and a cell phone as the external source of radiofrequency. The methodology is based on Maxwell’s equations and the finite element method to discretize our model, complemented by boundary integral techniques. Our results show the behavior and intensity of the electric and magnetic fields inside the brain as a function of distance. Furthermore, we aim to understand their influence on the action potential, which is in the millivolt range, with frequencies between 0.1 and 100 Hz.

关键词： Electric potential Action potentials Electromagnetic scattering Brain modeling Mathematical models Numerical models Finite element analysis

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Artificial Intelligence-Based Optimised Energy Management System for Microgrids 58

Artificial Intelligence-Based Optimised Energy Management Sy...

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58th International Universities Power engineering Conference, UPEC 2023

作者： Hussain, Muhammad Majid Nazir, Mian Hammad Akhtar, Muhammad Naveed Javed, Waqas Razaq, Abdul Pasha, Ahmad Hesham School of Engineering & Physical Sciences Heriot-Watt University Edinburgh United Kingdom University of South Wales Faculty of Computing Engineering & Science Cardiff United Kingdom Rachna College of Engineering & Technology Department of Electrical Engineering Gujranwala Pakistan James Watt School of Engineering University of Glasgow Glasgow United Kingdom School of Design and Informatics Abertay University Dundee United Kingdom

ISBN: (纸本)9798350316834

Electric power consistency is one of the essential variables in the social and economic growth of a smart city. In contrast, innovative energy sources and intelligent electricity networks are the primary components in making a city smart. This study describes an artificial neural network (ANN)- based controller for increasing power supply consistency by employing a dynamic voltage restorer. The optimisation approach particle swarm optimisation (PSO) is also discussed in this work, used to compute the maximum power point tracking (MPPT) of wind/photovoltaic hybrid power systems. The proposed PSO and ANN techniques can detect load, wind velocity, and solar irradiation fluctuations to optimise generating device power output, allowing hybrid power systems to function steadily, safely, and economically. This paper predicts and compares results with two cases using PSO and ANN to show the greater robustness and comparability of microgrid hybrid energies, namely photovoltaic and wind power. The simulation results show that the ANN-based controller with the PSO technique gives better performance as compared to the fuzzy controller. Further, the simulation results show that the converter can follow the hybrid system's maximum power point. © 2023 IEEE.

关键词： Neural networks

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Reducing the energy consumption required for the disassembly process implemented on an assembly and disassembly line 24

Reducing the energy consumption required for the disassembly...

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24th International Conference on Control Systems and Computer science, CSCS 2023

作者： Marius-Adrian, Paun Elena, Dragomir Otilia Florin, Dragomir Stelian, Iliescu Sergiu Eugenia, Minca Octavian Gabriel, Duca Valentin, Gurgu Ion University 'Politehnica' of Bucharest Department of Automation and Industrial Informatics Bucharest Romania Valahia University of Targoviste Faculty of Electrical Engineering Electronics and Information Technology Targoviste Romania University 'Politehnica ' of Bucharest Faculty of Automatic Control and Computers Bucharest Romania University 'Dunarea de Jos ' of Galati Department of Automation and Electrical Engineering Galati Romania Institute of Multidisciplinary Research for Science and Technology Valahia University of Targoviste Targoviste Romania

ISBN: (纸本)9798350313390

This paper proposes a method to reduce the energy consumption of an industrial disassembly process implemented on a production line by using an energy-efficient secondary disassembly process. In the case of defective products, the flow directs the simple product with priority to be disassembled in workstation (WS) 7. Therefore, all defective products are redirected to WS7. The disassembly of all simple products in WS7 is problematic for two reasons: the power consumption of WS7 is high compared to the other workstations, additionally there is an increased possibility of bottlenecks in the production line due to the high disassembly time. The proposed solution to reduce the possibility of deadlock and reduce power consumption is to redirect the defective products to the flexible cell (FC). Handling robots located in neighboring stations will participate collaboratively in the disassembly process. The control structure ensures that the two robots 'meet' in the boundary plane of WS3 with WS4 and disassemble each component through collaborative tasks. Collaborative control of disassembly is thought of as a solution to the global 'fluidization' of production, removing the bottleneck, from the overall flow, but also as an ingenious solution to reduce energy consumption. © 2023 IEEE.

关键词： Assembly

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